#include "lbpfeatures.h"
#include "cascadeclassifier.h"

CvLBPFeatureParams::CvLBPFeatureParams() {
	maxCatCount = 256;
	name = LBPF_NAME;
}

void CvLBPEvaluator::init(const CvFeatureParams* _featureParams, int _maxSampleCount, Size _winSize) {
	CV_Assert( _maxSampleCount > 0);
	sum.create((int)_maxSampleCount, (_winSize.width + 1) * (_winSize.height + 1), CV_32SC1);
	CvFeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
}

void CvLBPEvaluator::setImage(const Mat& img, uchar clsLabel, int idx) {
	CV_DbgAssert( !sum.empty() );
	CvFeatureEvaluator::setImage( img, clsLabel, idx );
	Mat innSum(winSize.height + 1, winSize.width + 1, sum.type(), sum.ptr<int>((int)idx));
	integral( img, innSum );
}

void CvLBPEvaluator::writeFeatures( FileStorage& fs, const Mat& featureMap ) const {
	_writeFeatures( features, fs, featureMap );
}

void CvLBPEvaluator::generateFeatures() {
	int offset = winSize.width + 1;
	for ( int x = 0; x < winSize.width; x++ )
		for ( int y = 0; y < winSize.height; y++ )
			for ( int w = 1; w <= winSize.width / 3; w++ )
				for ( int h = 1; h <= winSize.height / 3; h++ )
					if ( (x + 3 * w <= winSize.width) && (y + 3 * h <= winSize.height) ) {
						features.push_back( Feature(offset, x, y, w, h ) );
					}
	numFeatures = (int)features.size();
}

CvLBPEvaluator::Feature::Feature() {
	rect = cvRect(0, 0, 0, 0);
}

CvLBPEvaluator::Feature::Feature( int offset, int x, int y, int _blockWidth, int _blockHeight ) {
	Rect tr = rect = cvRect(x, y, _blockWidth, _blockHeight);
	CV_SUM_OFFSETS( p[0], p[1], p[4], p[5], tr, offset )
	tr.x += 2 * rect.width;
	CV_SUM_OFFSETS( p[2], p[3], p[6], p[7], tr, offset )
	tr.y += 2 * rect.height;
	CV_SUM_OFFSETS( p[10], p[11], p[14], p[15], tr, offset )
	tr.x -= 2 * rect.width;
	CV_SUM_OFFSETS( p[8], p[9], p[12], p[13], tr, offset )
}

void CvLBPEvaluator::Feature::write(FileStorage& fs) const {
	fs << CC_RECT << "[:" << rect.x << rect.y << rect.width << rect.height << "]";
}
